使用基于案例的推理,提高罕见疾病的诊断精度
Richard Noll1, Alexandra Berger2, Carlo Facchinello3
1Institute of Medical Informatics, Goethe University Frankfurt, University Medicine, 60590 Frankfurt am Main, Germany.
基于病例的推理 (CBR) 通过将新患者病例与历史数据进行比较,显示出对罕见疾病诊断的希望. 使用更广泛的ICD-10代码可以改善初始诊断指导和患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 罕见疾病的诊断 罕见疾病的诊断
背景情况:
- 由于罕见疾病的流行率较低和呈现方式多样化,它们带来了重大诊断挑战.
- 有效的诊断工具对于及时干预和改善罕见疾病病例患者的治疗结果至关重要.
- 基于案例的推理 (CBR) 提供了一个潜在的框架,可以利用患者历史数据来帮助诊断复杂的疾病.
研究的目的:
- 评估基于案例的推理 (CBR) 在增强罕见疾病的诊断过程中的有效性.
- 在CBR系统中对患者记录进行不同数据表示方法 (TF,TF-IDF,TF-IDF与语义嵌入) 的比较.
- 评估ICD-10代码细粒度对诊断预测准确性的影响.
主要方法:
- 使用了来自法兰克福大学医院的4295例患者病例的数据集,数据使用OMOP常用数据模型进行了标准化.
- 三种文本表示方法 (TF,TF-IDF,TF-IDF与语义向量嵌入) 应用于患者记录进行相似性搜索.
- 通过交叉验证,专家对概念相关性的评价以及对ICD-10代码细粒度效应的分析来评估诊断精度.
主要成果:
- TF-IDF方法实现了高诊断精度,在10个最相似的病例中,平均积极预测值为91%.
- 在评估的数据表示方法之间没有观察到统计学上显著的差异.
- 专家评估表明,高权重概念具有中度的医疗相关性,而增加的ICD-10代码细分度导致预测精度下降.
结论:
- CBR方法在多个医学专业中广泛适用于罕见疾病诊断.
- 在CBR系统中使用更广泛的ICD-10代码可以增强初始诊断指导,并可能改善患者的治疗结果.
- 建议进一步整合可解释人工智能和全面的实验室价值数据,以提高诊断透明度和系统实用性.
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